基于卡尔曼滤波的多传感器测量数据融合

Multisensor measured data fusion based on Kalman filtering

为解决最小二乘数据融合方法不能显式考虑测量的不确定性等问题,提出基于Kalman滤波的多传感器测量数据融合方法,此方法不仅显式考虑各测量设备的不确定性,而且还能实现单点和批量融合数据,有助于用户根据测量数据的多少选择有效的融合方法;且能有效地过滤基于Mahalanobis统计距离的异常噪声点.实例证明,此方法能获得高质量的融合曲面.

Because least square data fusion can not explicit consider the measurement uncertainties,the paper proposes the method of multisensor measured data fusion based on Kalman filtering.The method not only considers measurement uncertainties and also realizes processing the single measured datum and numbers of data so that customs choose efficient method of data fusion according to measured data.Finally,the method robustly identifies the noise variance and outliers based on the Mahalanobis distance.Experimental results demonstrate that the method produces better quality fusion surface.